多圈层陆面过程参数化研究中遥感信息应用的进展和方向

张佳华; 徐祥德; 延晓冬; 毛飞

多圈层陆面过程参数化研究中遥感信息应用的进展和方向

1.
中国气象科学研究院, 北京 100081
2.
中国科学院东亚区域气候环境重点实验室, 北京 100029

资助项目:

本文得到中国科学院知识创新工程重大项目 KZCX1-SW-01-11

国家自然科学基金 30370814

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- 被引次数: 0
出版历程
- 收稿日期: 2002-01-20
- 修回日期: 2003-05-08
- 刊出日期: 2003-12-31

THE ADVANCES AND DEVELOPMENTOF REMOTE SENSING APPLICATIONS TO LAND SURFACE PROCESS PARAMETERIZATION

1.
Chinese Academy of Meteorological Sciences, Beijing 100081
2.
Key Lab of Regional Climate-Environment for East Asia, Chinese Academy of Sciences, Beijing 100029

摘要

摘要: 近年来遥感技术的发展为多圈层中陆面过程和边界层研究提供了有力的工具。文章分析了目前用于陆面过程参数化研究的重要遥感信息源。并评述遥感信息在陆面过程参数化研究中的基本应用和存在问题，最后，提出发展方向和展望。
- 遥感信息;
- 陆面过程参数化;
- 能量;
- 水分;
- 生物地球学循环
Abstract: Recent years, many meteorologists and climatologists pay more attention to land surface condition, which effects on the changes of heat, water and matter between land surface vegetation and climate in the condition of global climate change. On the one hand, it is widely recognized that there is a close relationship between and terrestrial ecosystem in global scale; the climate variations have significant impact on the vegetation distributional pattern and its growing rate and development. On the other hand, the global vegetation has a prominent feedback on climate through surface roughness length, albedo and evapotranspiration. The development of AGCM and Regional Climate Model (RCM) plays an important role for us to understand the relationship between climate change and terrestrial ecosystems change. In these models, many key biophysical parameters are required to act as input variables for each model runs. However, over past few years' studies suggest that the spatial distribution patterns for some input biophysical parameters (e. g. LAI, canopy conductance of water vapor and CO2 etc.) and relationship with bio-climate are obscure. These conditions affect the simulating precision to a great extent. Hence, it is essential to carry out the studies of parameters derived from spatial satellite data. At present, the development of remote sensing technology has provided a useful tool to study the land surface eco-process. In the current research, the important remote sensing information data used to study land surface process are analyzed. Then, the main applications of remote sensing information to land surface process parameterization are reviewed. Finally, the main problem and development of remote sensing information used in land surface process parameterization are discussed.
- Remote sensing information;
- Land surface process parameterization;
- Energy;
- Moisture;
- Biogeochemical cycles

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参考文献(63)

[1]	1] Dickinson R E. Land processes in climate models. Remote Sens. Environ., 1995, 51: 27~38. doi: 10.1016/0034-4257(94)00062-R
[2]	Sellers P L, Randall D A, Collatz G J, et al. A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation. J. Clim., 1996, 9:676~705. doi: 10.1175/1520-0442(1996)009%3C0676%3AARLSPF%3E2.0.CO%3B2
[3]	Hall F G, Townshend J R, Engman E T. Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sens. Environ., 1995, 51(1):138~156. doi: 10.1016/0034-4257(94)00071-T
[4]	董超华, 张文建编.气象卫星遥感反演和应用论文集.北京:海洋出版社, 2001.1~541.
[5]	Rao P K等著. 许健民译. 气象卫星--系统、资料及其在环境中应用. 北京: 气象出版社, 1994.
[6]	Townshend J R G (Ed.). Improved Global Data for Land Applications: A Proposal fro a New High Resolution Data Set. IGBP Report 20, International Geosphere Biosphere, Stockholm, Sweden. 1992.
[7]	Gutman G G. Global data on land surface parameters from NOAA AVHRR frouse in numerical climate models. J. Clim., 1994, 7: 669~680. doi: 10.1175/1520-0442(1994)007 & lt; 0669:GDOLSP & gt; 2.0.CO; 2
[8]	James M, Kalluri S. The pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring. Int. J. Remote Sens., 1994, 15(17): 3347~3364. doi: 10.1080/01431169408954335
[9]	Defries R S, Townshend J R G. NDVI-derived land cover classifications at a global scale. Int. J. of Remote Sens., 1994, 15(17): 3567~3586. doi: 10.1080/01431169408954345
[10]	Sellers P J, Los S O, Tucker C J, et al, A revised land surface parameterization (SiB2) for atmospheric GCMs. Part II: The generation of global fields of terrestrial biophysical parameters from satellite data. J. Clim., 1996b, 9: 706~737. doi: 10.1175/1520-0442(1996)009 & lt; 0706:ARLSPF & gt; 2.0.CO; 2
[11]	Loveland T R, Reed B C, Brown J F, et al. Development of a global land cover characteristics database and IGBP DIS cover from 1-km AVHRR data. Int. J. of Remote Sens., 2000, 21: 1303~1330. doi: 10.1080/014311600210191
[12]	Los S O, Collatz G J, Sellers P J, et al. A global 9-year biophysical landsurface dataset from NOAA AVHRR data. J. Hydrometeorol., 2000, 1: 183~199. doi: 10.1175/1525-7541(2000)001 & lt; 0183:AGYBLS & gt; 2.0.CO; 2
[13]	周秀骥, 陶善昌, 姚克亚著.高等大气物理学.北京:气象出版社, 1991. 659~1249.
[14]	金亚秋著.电磁散射和热辐射的遥感理论.北京:科学出版社, 1993. 46~95.
[15]	郭华东等著.雷达对地观测理论与应用.北京:科学出版社, 2000. 1~530.
[16]	李小文, 汪骏发, 王锦地, 等著.多角度与热红外对地遥感.北京:科学出版社, 2001. 1~180.
[17]	Csiszar I, Gutman G. Mapping global land surface albedo from NOAA AVHRR. J. Geophy.Res., 1999, 104: 6215~6228. doi: 10.1029/1998JD200090
[18]	Lucht W, Strahler A H, Schaaf C B. An algorithm for the retrieval of albedo from space using semiempirical BRDF models. IEEE T. Geosci. Rem. Sens., 2000, 38: 977~998. doi: 10.1109/36.841980
[19]	Strugnell N C. A global albedo data set derived from AVHRR data for use in climate simulations. Geophy. Res. Lett., 2001, 28(1): 191~194. doi: 10.1029/2000GL011580
[20]	田国良, 徐兴奎, 柳钦火.用于地表能量交换的动态地表特征模式.遥感学报, 2000, (4):121~128.
[21]	刘玉洁, 杨忠东编著. MODIS遥感信息处理原理与算法.北京:科学出版社, 2001. 144~179.
[22]	Bishop J K, Rossow W B, Dutton E G. Surface solar irradiance from the International Satellite Cloud Climatology Project 1983-1991. J. Geophy. Res., 1997, 102: 6883~6910. doi: 10.1029/96JD03865
[23]	Ignatov A, Nalli N. Aerosol Retrievals from Multi-Year Multi-Satellite AVHRR Pathfinder Atmosphere (PATMOS) Dataset for Correcting Remotely Sensed Sea Surface Temperatures. JTECH, 2002.
[24]	Goward S N, Huemmrich K F. Vegetation canopy PAR absorbance and the normalized difference vegetation index: An assessment using the SAIL model. Remote Sens. Environ., 1992, 39: 119~140. doi: 10.1016/0034-4257(92)90131-3
[25]	Ferraro R R, Weng F Z, Grody N C, et al. An eight year (1987-1994) time series of rainfall, clouds, water vapor, snow cover, and sea ice derived from SSM/I measurement. Bull. Amer. Meteor. Soc., 1996, 77: 891~906. doi: 10.1175/1520-0477(1996)077 & lt; 0891:AEYTSO & gt; 2.0.CO; 2
[26]	Cheng Minghu, He Huizhong, Mao Dongyan, et al, Study of 1998 heavy rainfall over the Yangtze river basin using TRMM data. Advance in Atmospheric physics, 2001, 18(3): 387~396.
[27]	刘黎平, 葛润生, 张沛源.双线偏振多普勒天气雷达遥测降水强度和液态含水量的方法和精度研究.大气科学, 2002, 26(5):702~710. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200205011.htm
[28]	Choudhury B J. Global pattern of potential evaporation calculated from the Penman-Monteith equation using satellite and assimilated data. Remote Sens. Environ., 1997, 61: 64~81. doi: 10.1016/S0034-4257(96)00241-6
[29]	Norman J M, Kustas W P, Humes K S. A two-source approach for estimating soil and vegetation energy fluxes from observations of directional radiometric surface temperature. Agric. For. Meteorol., 1995, 77: 263~293. doi: 10.1016/0168-1923(95)02265-Y
[30]	张仁华, 孙晓敏, 朱治林, 等.以微分热惯量为基础的地表蒸发全遥感信息模型及在甘肃沙坡头地区的验证.中国科学D辑, 2002, 32(12): 1041~1410. http://www.cnki.com.cn/Article/CJFDTOTAL-JDXK200212008.htm
[31]	Goetz S J. Multi-sensor analysis of NDVI, surface temperature and biophysical variable at a mixed grassland site. Int. J. Remote Sensing, 1997, 18: 71~94. doi: 10.1080/014311697219286
[32]	Wan Z, Dozier J. A generalized split-window algorithm for retrieving land surface temperature from space. IEEE Trans. Geosci. Remote Sens. 1996, 34(4): 892~905. doi: 10.1109/36.508406
[33]	Price J C. Land surface temperature measurements from the split window channels of the NOAA-7AVHRR. J. Geophys. Res., 1984, 89, 7231~7237. doi: 10.1029/JD089iD05p07231
[34]	Ulivieri C M M. A split-window algorithm for estimating land surface temperature from satellites. Advances in Space Research, 1994, 14: 59. doi: 10.1016/0273-1177(94)90193-7
[35]	Van de Griend A A, Ans Owe M. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. International J. of Remote Sensing, 1993, 14: 1119~1131. doi: 10.1080/01431169308904400
[36]	汪宏七, 赵高祥, 王立志.陆面温度的反演算法和大气订正的影响.红外与毫米波学报, 2000, 19(1): 48~52. http://www.cnki.com.cn/Article/CJFDTOTAL-HWYH200001011.htm
[37]	Ceccato P, et al. Designing a spectral index to estimate vegetation water content from remote sensing data, Part 1: Theoretial approach. Remote Sens. Environ, 2002, 82:188~197. doi: 10.1016/S0034-4257(02)00037-8
[38]	Sano E E, Moran M S, Huete A R, et al. C-and multiangle Ku-band SAR data for bare soil moisture estimation in agricultural areas. Remote Sens. Environ, 1999, 64: 77~90.
[39]	Lipton A E, Inc. A E R, Lexington M A, et al. Retrieval of water vapor over land surfaces from microwave measurements. The 83rd Annual Meeting, Long Beach, CA, 2003.
[40]	Laymon C A, USRA, Huntsville A L, et al. Soil moisture measurements and modeling for validating AMSR-E soil moisture products. Observing and Understanding the Variability of Water in Weather and Climate (Compact View). The 83rd Annual Meeting Long Beach, CA, 2003.
[41]	Weng F, Ferraro R R, Grody N C. Effects of AMSU-A cross track asymmetry of brightness temperatures on retrieval of atmospheric and surface parameters. Microwave Radiometry and Remote Sensing of the Earth's Surface and Atmosphere, 1999.
[42]	强风暴实验室.大气遥感技术论文集.北京:气象出版社, 1997. 1~130.
[43]	Xu X, Liu Q, Chen J. Synchronous retrieval of land surface temperature and emissivity. Science in China (Series D), 1998, 41(6):658~668. doi: 10.1007/BF02878749
[44]	Valor E, Vicente Caselles. Mapping land surface emissivity from NDVI: Application to European, African, and South American Areas. Remote Sens. Environ., 1996, 57:167~184. doi: 10.1016/0034-4257(96)00039-9
[45]	Wan Z, Li Z L. Physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Trans. Geosci. Remote Sens., 1997, 35(4): 980~996. doi: 10.1109/36.602541
[46]	刘玉洁, 王丽波, 刘诚, 等.我国气象卫星积雪监测的新进展.卫星应用, 1998, 6(4).
[47]	Kwok R, Nghiem S V, Yueh S H, et al. Retrieval of thin ice thickness from multifrequency polarimetric SAR data. Remote Sens.Environ., 1995, 51:361~374. doi: 10.1016/0034-4257(94)00017-H
[48]	张佳华, 符淙斌, 王长耀.遥感信息结合植物光合生理特性研究区域作物产量水分胁迫模型.大气科学, 2000, 24(5):683~693. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200005014.htm
[49]	张佳华, 符淙斌.利用卫星反演的叶面积指数研究中国东部植被对东亚季风的响应.自然科学进展, 2002, 12(10):1098~1101. http://www.cnki.com.cn/Article/CJFDTOTAL-ZKJZ200210024.htm
[50]	Zhang Jiahua, Fu Congbin, Yan Xiaodong. A global reseondence analysis of LAI versus surface air temperature and precipitation variations. Chinese J. of Geophysics, 2002, 45(5):662~669. doi: 10.1002/cjg2.v45.5
[51]	Moulin S, Kergoat L, Viovy N, et al. Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements. J. Clim., 1997, 10:1154~1170. doi: 10.1175/1520-0442(1997)010 & lt; 1154:GSAOVP & gt; 2.0.CO; 2
[52]	Running S W, Loverland T R, Pierce L L, et al. A remote sensing based vegetation classification logic for global land cover analysis. Remote Sens. Environ. 1995, 51: 39~48. doi: 10.1016/0034-4257(94)00063-S
[53]	Pinty B, Verstraete M M. GEMI: A non-linear index to monitor global vegetation from satellites. Vegetation, 1992, 10(1): 15~20.
[54]	Huete A R. A soil adjusted vegetation index (SAVI). Remote Sens. Environ., 1988, 25: 295~309. doi: 10.1016/0034-4257(88)90106-X
[55]	Qi J, Chehbouni A, Huete A R, et al. Modified soil adjusted vegetation index (MSAVI). Remote Sens. of Environ., 1994, 48: 119~126. doi: 10.1016/0034-4257(94)90134-1
[56]	Steven M D. The sensitivity of the OSAVI vegetation index to observational parameter. Remote Sens. of Environ., 1998, 63:49~60. doi: 10.1016/S0034-4257(97)00114-4
[57]	Kaufman Y J, Tanre D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Tans. Geos. Remote Sensing, 1992, 30(2):231~248. doi: 10.1109/36.134074
[58]	Goetz S J, Prince S D, Small J, et al. Interannual variability of global terrestrial primary production: Results of a model driven with satellite observations. J.Geophys.Res., 2000, 105:20077~20091. doi: 10.1029/2000JD900274
[59]	Woodward F I, Smith T M, Emanuel W R. A global land primary productivity and phytogeography model. Global biogeochem. Cycles, 1995, 9: 471~49. doi: 10.1029/95GB02432
[60]	张佳华, 王长耀, 符淙斌. CO2倍增下遥感-光合作物产量响应模型的研究与应用.遥感学报, 2000, (4):46~50. http://www.cnki.com.cn/Article/CJFDTOTAL-YGXB200001008.htm
[61]	Rodell M, Houder P R, Jambor U, et al. The global land data assimilation system. Submitted to Bull.Amer. Meteor. Soc., 2002.
[62]	徐祥德, 许健民, 王继志, 等著.大气遥感再分析场构造技术与原理.北京:气象出版社, 2003.
[63]	David Carbon. International Commitment & Co-operation: Keys to a successful CEOP. CEOP, Newletter, 2003, (3): 1~2.